{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":1,"outputs":[{"output_type":"stream","text":"/kaggle/input/machine-hack-house-price-prediction/Test.csv\n/kaggle/input/machine-hack-house-price-prediction/sample_submission.csv\n/kaggle/input/machine-hack-house-price-prediction/Train.csv\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#importing the libraries\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom catboost import CatBoostRegressor\nfrom xgboost import XGBRegressor\nfrom lightgbm import LGBMRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_log_error\nfrom sklearn.model_selection import KFold\n%matplotlib inline","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# to see all the comands result in a single kernal \n%load_ext autoreload\n%autoreload 2\nfrom IPython.core.interactiveshell import InteractiveShell\nInteractiveShell.ast_node_interactivity = \"all\"","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# to increase no. of rows and column visibility in outputs\npd.set_option('display.max_rows', 500)\npd.set_option('display.max_columns', 500)\npd.set_option('display.width', 1000)","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=pd.read_csv(\"../input/machine-hack-house-price-prediction/Train.csv\")\ntest=pd.read_csv(\"../input/machine-hack-house-price-prediction/Test.csv\")\nsub=pd.read_csv('../input/machine-hack-house-price-prediction/sample_submission.csv')\ntrain.shape\ntest.shape\nsub.shape","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"(29451, 12)"},"metadata":{}},{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"(68720, 11)"},"metadata":{}},{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"(68720, 1)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sub.head()\ntrain.head()\ntrain.tail()\ntest.head()\ntest.tail()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"   TARGET(PRICE_IN_LACS)\n0                    0.0\n1                    0.0\n2                    0.0\n3                    0.0\n4                    0.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>TARGET(PRICE_IN_LACS)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  READY_TO_MOVE  RESALE                      ADDRESS  LONGITUDE   LATITUDE  TARGET(PRICE_IN_LACS)\n0     Owner                   0     0        2       BHK  1300.236407              1       1        Ksfc Layout,Bangalore  12.969910  77.597960                   55.0\n1    Dealer                   0     0        2       BHK  1275.000000              1       1    Vishweshwara Nagar,Mysore  12.274538  76.644605                   51.0\n2     Owner                   0     0        2       BHK   933.159722              1       1             Jigani,Bangalore  12.778033  77.632191                   43.0\n3     Owner                   0     1        2       BHK   929.921143              1       1  Sector-1 Vaishali,Ghaziabad  28.642300  77.344500                   62.5\n4    Dealer                   1     0        2       BHK   999.009247              0       1             New Town,Kolkata  22.592200  88.484911                   60.5","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>POSTED_BY</th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>BHK_OR_RK</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>ADDRESS</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n      <th>TARGET(PRICE_IN_LACS)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>1300.236407</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Ksfc Layout,Bangalore</td>\n      <td>12.969910</td>\n      <td>77.597960</td>\n      <td>55.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>1275.000000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Vishweshwara Nagar,Mysore</td>\n      <td>12.274538</td>\n      <td>76.644605</td>\n      <td>51.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>933.159722</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Jigani,Bangalore</td>\n      <td>12.778033</td>\n      <td>77.632191</td>\n      <td>43.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>929.921143</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Sector-1 Vaishali,Ghaziabad</td>\n      <td>28.642300</td>\n      <td>77.344500</td>\n      <td>62.5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Dealer</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>999.009247</td>\n      <td>0</td>\n      <td>1</td>\n      <td>New Town,Kolkata</td>\n      <td>22.592200</td>\n      <td>88.484911</td>\n      <td>60.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"      POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  READY_TO_MOVE  RESALE                            ADDRESS  LONGITUDE   LATITUDE  TARGET(PRICE_IN_LACS)\n29446     Owner                   0     0        3       BHK  2500.000000              1       1               Shamshabad Road,Agra  27.140626  78.043277                   45.0\n29447     Owner                   0     0        2       BHK   769.230769              1       1  E3-108, Lake View Recidency,,Vapi  39.945409 -86.150721                   16.0\n29448    Dealer                   0     0        2       BHK  1022.641509              1       1                  Ajmer Road,Jaipur  26.928785  75.828002                   27.1\n29449     Owner                   0     0        2       BHK   927.079009              1       1             Sholinganallur,Chennai  12.900150  80.227910                   67.0\n29450    Dealer                   0     1        2       BHK   896.774194              1       1                   Jagatpura,Jaipur  26.832353  75.841749                   27.8","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>POSTED_BY</th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>BHK_OR_RK</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>ADDRESS</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n      <th>TARGET(PRICE_IN_LACS)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>29446</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>BHK</td>\n      <td>2500.000000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Shamshabad Road,Agra</td>\n      <td>27.140626</td>\n      <td>78.043277</td>\n      <td>45.0</td>\n    </tr>\n    <tr>\n      <th>29447</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>769.230769</td>\n      <td>1</td>\n      <td>1</td>\n      <td>E3-108, Lake View Recidency,,Vapi</td>\n      <td>39.945409</td>\n      <td>-86.150721</td>\n      <td>16.0</td>\n    </tr>\n    <tr>\n      <th>29448</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>1022.641509</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Ajmer Road,Jaipur</td>\n      <td>26.928785</td>\n      <td>75.828002</td>\n      <td>27.1</td>\n    </tr>\n    <tr>\n      <th>29449</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>927.079009</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Sholinganallur,Chennai</td>\n      <td>12.900150</td>\n      <td>80.227910</td>\n      <td>67.0</td>\n    </tr>\n    <tr>\n      <th>29450</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>896.774194</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Jagatpura,Jaipur</td>\n      <td>26.832353</td>\n      <td>75.841749</td>\n      <td>27.8</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"  POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK    SQUARE_FT  READY_TO_MOVE  RESALE             ADDRESS  LONGITUDE   LATITUDE\n0     Owner                   0     0        1       BHK   545.171340              1       1        Kamrej,Surat  21.262000  73.047700\n1    Dealer                   1     1        2       BHK   800.000000              0       0     Panvel,Lalitpur  18.966114  73.148278\n2    Dealer                   0     0        2       BHK  1257.096513              1       1    New Town,Kolkata  22.592200  88.484911\n3    Dealer                   0     0        3       BHK  1400.329489              1       1  Kalwar Road,Jaipur  26.988300  75.584600\n4     Owner                   0     0        1       BHK   430.477830              1       1   Mai Mandir,Nadiad  22.700000  72.870000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>POSTED_BY</th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>BHK_OR_RK</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>ADDRESS</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>BHK</td>\n      <td>545.171340</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Kamrej,Surat</td>\n      <td>21.262000</td>\n      <td>73.047700</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Dealer</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>800.000000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Panvel,Lalitpur</td>\n      <td>18.966114</td>\n      <td>73.148278</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>1257.096513</td>\n      <td>1</td>\n      <td>1</td>\n      <td>New Town,Kolkata</td>\n      <td>22.592200</td>\n      <td>88.484911</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>BHK</td>\n      <td>1400.329489</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Kalwar Road,Jaipur</td>\n      <td>26.988300</td>\n      <td>75.584600</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Owner</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>BHK</td>\n      <td>430.477830</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Mai Mandir,Nadiad</td>\n      <td>22.700000</td>\n      <td>72.870000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"      POSTED_BY  UNDER_CONSTRUCTION  RERA  BHK_NO. BHK_OR_RK     SQUARE_FT  READY_TO_MOVE  RESALE                             ADDRESS  LONGITUDE   LATITUDE\n68715    Dealer                   0     1        2       BHK    856.555505              1       1              Thane West,Maharashtra  19.180000  72.963330\n68716    Dealer                   0     1        3       BHK   2304.147465              1       1            Sector-66A Mohali,Mohali  30.661104  76.746082\n68717    Dealer                   1     1        1       BHK  33362.792750              0       0                  Balkum,Maharashtra  19.222101  72.988231\n68718    Dealer                   0     0        2       BHK   1173.708920              1       1                       Hadapsar,Pune  18.496670  73.941670\n68719    Dealer                   0     0        3       BHK   2439.532944              1       1  Hiranandani Gardens - Powai,Mumbai  19.124896  72.893503","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>POSTED_BY</th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>BHK_OR_RK</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>ADDRESS</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>68715</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>856.555505</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Thane West,Maharashtra</td>\n      <td>19.180000</td>\n      <td>72.963330</td>\n    </tr>\n    <tr>\n      <th>68716</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>1</td>\n      <td>3</td>\n      <td>BHK</td>\n      <td>2304.147465</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Sector-66A Mohali,Mohali</td>\n      <td>30.661104</td>\n      <td>76.746082</td>\n    </tr>\n    <tr>\n      <th>68717</th>\n      <td>Dealer</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>BHK</td>\n      <td>33362.792750</td>\n      <td>0</td>\n      <td>0</td>\n      <td>Balkum,Maharashtra</td>\n      <td>19.222101</td>\n      <td>72.988231</td>\n    </tr>\n    <tr>\n      <th>68718</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>BHK</td>\n      <td>1173.708920</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Hadapsar,Pune</td>\n      <td>18.496670</td>\n      <td>73.941670</td>\n    </tr>\n    <tr>\n      <th>68719</th>\n      <td>Dealer</td>\n      <td>0</td>\n      <td>0</td>\n      <td>3</td>\n      <td>BHK</td>\n      <td>2439.532944</td>\n      <td>1</td>\n      <td>1</td>\n      <td>Hiranandani Gardens - Powai,Mumbai</td>\n      <td>19.124896</td>\n      <td>72.893503</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Missing Values\ntrain.isnull().sum().sum()\ntest.isnull().sum().sum()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"0"},"metadata":{}},{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"0"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.describe()\ntest.describe()","execution_count":8,"outputs":[{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"       UNDER_CONSTRUCTION          RERA       BHK_NO.     SQUARE_FT  READY_TO_MOVE        RESALE     LONGITUDE      LATITUDE  TARGET(PRICE_IN_LACS)\ncount        29451.000000  29451.000000  29451.000000  2.945100e+04   29451.000000  29451.000000  29451.000000  29451.000000           29451.000000\nmean             0.179756      0.317918      2.392279  1.980217e+04       0.820244      0.929578     21.300255     76.837695             142.898746\nstd              0.383991      0.465675      0.879091  1.901335e+06       0.383991      0.255861      6.205306     10.557747             656.880713\nmin              0.000000      0.000000      1.000000  3.000000e+00       0.000000      0.000000    -37.713008   -121.761248               0.250000\n25%              0.000000      0.000000      2.000000  9.000211e+02       1.000000      1.000000     18.452663     73.798100              38.000000\n50%              0.000000      0.000000      2.000000  1.175057e+03       1.000000      1.000000     20.750000     77.324137              62.000000\n75%              0.000000      1.000000      3.000000  1.550688e+03       1.000000      1.000000     26.900926     77.828740             100.000000\nmax              1.000000      1.000000     20.000000  2.545455e+08       1.000000      1.000000     59.912884    152.962676           30000.000000","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n      <th>TARGET(PRICE_IN_LACS)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>29451.000000</td>\n      <td>29451.000000</td>\n      <td>29451.000000</td>\n      <td>2.945100e+04</td>\n      <td>29451.000000</td>\n      <td>29451.000000</td>\n      <td>29451.000000</td>\n      <td>29451.000000</td>\n      <td>29451.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.179756</td>\n      <td>0.317918</td>\n      <td>2.392279</td>\n      <td>1.980217e+04</td>\n      <td>0.820244</td>\n      <td>0.929578</td>\n      <td>21.300255</td>\n      <td>76.837695</td>\n      <td>142.898746</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.383991</td>\n      <td>0.465675</td>\n      <td>0.879091</td>\n      <td>1.901335e+06</td>\n      <td>0.383991</td>\n      <td>0.255861</td>\n      <td>6.205306</td>\n      <td>10.557747</td>\n      <td>656.880713</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>3.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-37.713008</td>\n      <td>-121.761248</td>\n      <td>0.250000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>9.000211e+02</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>18.452663</td>\n      <td>73.798100</td>\n      <td>38.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>1.175057e+03</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>20.750000</td>\n      <td>77.324137</td>\n      <td>62.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.550688e+03</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>26.900926</td>\n      <td>77.828740</td>\n      <td>100.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>20.000000</td>\n      <td>2.545455e+08</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>59.912884</td>\n      <td>152.962676</td>\n      <td>30000.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}},{"output_type":"execute_result","execution_count":8,"data":{"text/plain":"       UNDER_CONSTRUCTION          RERA       BHK_NO.     SQUARE_FT  READY_TO_MOVE        RESALE     LONGITUDE      LATITUDE\ncount        68720.000000  68720.000000  68720.000000  6.872000e+04   68720.000000  68720.000000  68720.000000  68720.000000\nmean             0.176557      0.316531      2.388198  2.762419e+03       0.823443      0.933498     21.288045     76.919388\nstd              0.381296      0.465126      0.864577  1.640991e+05       0.381296      0.249159      6.179034     10.100950\nmin              0.000000      0.000000      1.000000  1.000000e+00       0.000000      0.000000    -38.391261   -118.354100\n25%              0.000000      0.000000      2.000000  9.000310e+02       1.000000      1.000000     18.452663     73.798100\n50%              0.000000      0.000000      2.000000  1.174982e+03       1.000000      1.000000     20.933260     77.324966\n75%              0.000000      1.000000      3.000000  1.550265e+03       1.000000      1.000000     26.888396     78.000723\nmax              1.000000      1.000000     31.000000  4.016393e+07       1.000000      1.000000     65.183330    175.278040","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>68720.000000</td>\n      <td>68720.000000</td>\n      <td>68720.000000</td>\n      <td>6.872000e+04</td>\n      <td>68720.000000</td>\n      <td>68720.000000</td>\n      <td>68720.000000</td>\n      <td>68720.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>0.176557</td>\n      <td>0.316531</td>\n      <td>2.388198</td>\n      <td>2.762419e+03</td>\n      <td>0.823443</td>\n      <td>0.933498</td>\n      <td>21.288045</td>\n      <td>76.919388</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.381296</td>\n      <td>0.465126</td>\n      <td>0.864577</td>\n      <td>1.640991e+05</td>\n      <td>0.381296</td>\n      <td>0.249159</td>\n      <td>6.179034</td>\n      <td>10.100950</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-38.391261</td>\n      <td>-118.354100</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>9.000310e+02</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>18.452663</td>\n      <td>73.798100</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.000000</td>\n      <td>1.174982e+03</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>20.933260</td>\n      <td>77.324966</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>1.550265e+03</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>26.888396</td>\n      <td>78.000723</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>31.000000</td>\n      <td>4.016393e+07</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>65.183330</td>\n      <td>175.278040</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.info()\ntest.info()","execution_count":9,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 29451 entries, 0 to 29450\nData columns (total 12 columns):\n #   Column                 Non-Null Count  Dtype  \n---  ------                 --------------  -----  \n 0   POSTED_BY              29451 non-null  object \n 1   UNDER_CONSTRUCTION     29451 non-null  int64  \n 2   RERA                   29451 non-null  int64  \n 3   BHK_NO.                29451 non-null  int64  \n 4   BHK_OR_RK              29451 non-null  object \n 5   SQUARE_FT              29451 non-null  float64\n 6   READY_TO_MOVE          29451 non-null  int64  \n 7   RESALE                 29451 non-null  int64  \n 8   ADDRESS                29451 non-null  object \n 9   LONGITUDE              29451 non-null  float64\n 10  LATITUDE               29451 non-null  float64\n 11  TARGET(PRICE_IN_LACS)  29451 non-null  float64\ndtypes: float64(4), int64(5), object(3)\nmemory usage: 2.7+ MB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 68720 entries, 0 to 68719\nData columns (total 11 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   POSTED_BY           68720 non-null  object \n 1   UNDER_CONSTRUCTION  68720 non-null  int64  \n 2   RERA                68720 non-null  int64  \n 3   BHK_NO.             68720 non-null  int64  \n 4   BHK_OR_RK           68720 non-null  object \n 5   SQUARE_FT           68720 non-null  float64\n 6   READY_TO_MOVE       68720 non-null  int64  \n 7   RESALE              68720 non-null  int64  \n 8   ADDRESS             68720 non-null  object \n 9   LONGITUDE           68720 non-null  float64\n 10  LATITUDE            68720 non-null  float64\ndtypes: float64(3), int64(5), object(3)\nmemory usage: 5.8+ MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#there are duplicates between test and train, for those rows we can directly use train target values\n#storing the target values of common rows in test and train\ntemp1=''\nfor i in test.columns:\n    temp1=temp1+train[i].astype(str)\ntrain['ALL']=temp1\ntemp2=''\nfor i in test.columns:\n    temp2=temp2+test[i].astype(str)\ntest['ALL']=temp2\nencoding=train.groupby('ALL')['TARGET(PRICE_IN_LACS)'].median()\ny_from_train=test['ALL'].map(encoding)","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#no. of rows which were directly replaced\nlen(test)-y_from_train.isnull().sum()","execution_count":11,"outputs":[{"output_type":"execute_result","execution_count":11,"data":{"text/plain":"2286"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=train.drop('ALL',1)\ntest=test.drop('ALL',1)","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['POSTED_BY'].value_counts()\ntest['POSTED_BY'].value_counts()","execution_count":13,"outputs":[{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"Dealer     18291\nOwner      10538\nBuilder      622\nName: POSTED_BY, dtype: int64"},"metadata":{}},{"output_type":"execute_result","execution_count":13,"data":{"text/plain":"Dealer     42437\nOwner      24920\nBuilder     1363\nName: POSTED_BY, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(train['POSTED_BY']);","execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['UNDER_CONSTRUCTION'].value_counts()\ntest['UNDER_CONSTRUCTION'].value_counts()","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"0    24157\n1     5294\nName: UNDER_CONSTRUCTION, dtype: int64"},"metadata":{}},{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"0    56587\n1    12133\nName: UNDER_CONSTRUCTION, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(train['UNDER_CONSTRUCTION']);","execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['RERA'].unique()\ntest['RERA'].unique()","execution_count":17,"outputs":[{"output_type":"execute_result","execution_count":17,"data":{"text/plain":"array([0, 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(train['BHK_NO.']);","execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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    13324\n3     10546\n1      3574\n4      1723\n5       190\n6        52\n7        11\n8        10\n15        4\n10        4\n20        4\n12        3\n9         3\n13        1\n11        1\n17        1\nName: BHK_NO., dtype: int64"},"metadata":{}},{"output_type":"execute_result","execution_count":20,"data":{"text/plain":"2     30998\n3     24756\n1      8383\n4      4001\n5       394\n6       106\n7        17\n8        15\n9        13\n12       13\n10        8\n20        6\n11        4\n13        3\n16        1\n18        1\n31        1\nName: BHK_NO., dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.groupby('BHK_NO.')['TARGET(PRICE_IN_LACS)'].mean()","execution_count":36,"outputs":[{"output_type":"execute_result","execution_count":36,"data":{"text/plain":"BHK_NO.\n1    3.557568\n2    4.008759\n3    4.515954\n4    5.335098\n5    5.798208\n6    5.882959\nName: TARGET(PRICE_IN_LACS), dtype: float64"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"Merging Classes of feature BHK_NO."},{"metadata":{"trusted":true},"cell_type":"code","source":"train.loc[train['BHK_NO.']>5,'BHK_NO.']=6\ntest.loc[test['BHK_NO.']>5,'BHK_NO.']=6","execution_count":21,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.groupby('BHK_NO.')['TARGET(PRICE_IN_LACS)'].mean()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.boxplot(train['SQUARE_FT']);","execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(train[train['SQUARE_FT']<100]['SQUARE_FT'],train[train['SQUARE_FT']<100]['TARGET(PRICE_IN_LACS)']);","execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(20,5))\nplt.hist(train[train['SQUARE_FT']<100]['SQUARE_FT'],bins=20);","execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1440x360 with 1 Axes>","image/png":"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   24157\n0     5294\nName: READY_TO_MOVE, dtype: int64"},"metadata":{}},{"output_type":"execute_result","execution_count":28,"data":{"text/plain":"1    56587\n0    12133\nName: READY_TO_MOVE, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['RESALE'].value_counts()\ntest['RESALE'].value_counts()","execution_count":29,"outputs":[{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"1    27377\n0     2074\nName: RESALE, dtype: int64"},"metadata":{}},{"output_type":"execute_result","execution_count":29,"data":{"text/plain":"1    64150\n0     4570\nName: RESALE, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"#encoding object features\ntrain['POSTED_BY']=train['POSTED_BY'].replace({'Dealer':2,'Owner':1,'Builder':0})\ntest['POSTED_BY']=test['POSTED_BY'].replace({'Dealer':2,'Owner':1,'Builder':0})\ntrain['BHK_OR_RK']=train['BHK_OR_RK'].replace({'BHK':1,'RK':0})\ntest['BHK_OR_RK']=test['BHK_OR_RK'].replace({'BHK':1,'RK':0})","execution_count":30,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.distplot(train['TARGET(PRICE_IN_LACS)']);","execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['TARGET(PRICE_IN_LACS)']=np.log1p(train['TARGET(PRICE_IN_LACS)'])\nsns.distplot(train['TARGET(PRICE_IN_LACS)']);","execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.violinplot(train['TARGET(PRICE_IN_LACS)']);","execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(x=train['SQUARE_FT'],y=train['TARGET(PRICE_IN_LACS)']);","execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(train['BHK_NO.'],train['SQUARE_FT'],hue=train['TARGET(PRICE_IN_LACS)']);","execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['SQUARE_FT_PER_ROOM']=train['SQUARE_FT']/train['BHK_NO.']\ntest['SQUARE_FT_PER_ROOM']=test['SQUARE_FT']/test['BHK_NO.']","execution_count":37,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['SQUARE_FT_ROOM']=train['SQUARE_FT']*train['BHK_NO.']\ntest['SQUARE_FT_ROOM']=test['SQUARE_FT']*test['BHK_NO.']","execution_count":38,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LATITUDE']=train['LONGITUDE']+train['LATITUDE']\ntrain['LONGITUDE']=train['LATITUDE']-train['LONGITUDE']\ntrain['LATITUDE']=train['LATITUDE']-train['LONGITUDE']\n\ntest['LATITUDE']=test['LONGITUDE']+test['LATITUDE']\ntest['LONGITUDE']=test['LATITUDE']-test['LONGITUDE']\ntest['LATITUDE']=test['LATITUDE']-test['LONGITUDE']","execution_count":39,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.scatterplot(train['LONGITUDE'],train['LATITUDE'],hue=train['TARGET(PRICE_IN_LACS)']);","execution_count":40,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"Extracting locality and city from address"},{"metadata":{"trusted":true},"cell_type":"code","source":"import re\ndef get_city(text):\n    return text.split(',')[-1]\ntrain['CITY']=train['ADDRESS'].apply(get_city)\ntest['CITY']=test['ADDRESS'].apply(get_city)","execution_count":45,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_locality(text):\n    return text.split(',')[-2]\ntrain['LOCALITY']=train['ADDRESS'].apply(get_locality)\ntest['LOCALITY']=test['ADDRESS'].apply(get_locality)","execution_count":46,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.info()\ntest.info()","execution_count":47,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 29451 entries, 0 to 29450\nData columns (total 16 columns):\n #   Column                 Non-Null Count  Dtype  \n---  ------                 --------------  -----  \n 0   POSTED_BY              29451 non-null  int64  \n 1   UNDER_CONSTRUCTION     29451 non-null  int64  \n 2   RERA                   29451 non-null  int64  \n 3   BHK_NO.                29451 non-null  int64  \n 4   BHK_OR_RK              29451 non-null  int64  \n 5   SQUARE_FT              29451 non-null  float64\n 6   READY_TO_MOVE          29451 non-null  int64  \n 7   RESALE                 29451 non-null  int64  \n 8   ADDRESS                29451 non-null  object \n 9   LONGITUDE              29451 non-null  float64\n 10  LATITUDE               29451 non-null  float64\n 11  TARGET(PRICE_IN_LACS)  29451 non-null  float64\n 12  SQUARE_FT_PER_ROOM     29451 non-null  float64\n 13  SQUARE_FT_ROOM         29451 non-null  float64\n 14  CITY                   29451 non-null  object \n 15  LOCALITY               29451 non-null  object \ndtypes: float64(6), int64(7), object(3)\nmemory usage: 3.6+ MB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 68720 entries, 0 to 68719\nData columns (total 15 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   POSTED_BY           68720 non-null  int64  \n 1   UNDER_CONSTRUCTION  68720 non-null  int64  \n 2   RERA                68720 non-null  int64  \n 3   BHK_NO.             68720 non-null  int64  \n 4   BHK_OR_RK           68720 non-null  int64  \n 5   SQUARE_FT           68720 non-null  float64\n 6   READY_TO_MOVE       68720 non-null  int64  \n 7   RESALE              68720 non-null  int64  \n 8   ADDRESS             68720 non-null  object \n 9   LONGITUDE           68720 non-null  float64\n 10  LATITUDE            68720 non-null  float64\n 11  SQUARE_FT_PER_ROOM  68720 non-null  float64\n 12  SQUARE_FT_ROOM      68720 non-null  float64\n 13  CITY                68720 non-null  object \n 14  LOCALITY            68720 non-null  object \ndtypes: float64(5), int64(7), object(3)\nmemory usage: 7.9+ MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_SF_MEAN']=train['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].mean())\ntest['LOCALITY_SF_MEAN']=test['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                         .groupby('LOCALITY')['SQUARE_FT'].mean())","execution_count":48,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_SF_MIN']=train['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].min())\ntest['LOCALITY_SF_MIN']=test['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].min())","execution_count":49,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_SF_MAX']=train['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].max())\ntest['LOCALITY_SF_MAX']=test['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].max())","execution_count":50,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_SF_STD']=train['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].std())\ntest['LOCALITY_SF_STD']=test['LOCALITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('LOCALITY')['SQUARE_FT'].std())","execution_count":51,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_FT_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['LOCALITY','BHK_OR_RK'])['SQUARE_FT'].transform('mean')[:29451]\ntest['LOCALITY_FT_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['LOCALITY','BHK_OR_RK'])['SQUARE_FT'].transform('mean')[29451:]","execution_count":52,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_PB_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['LOCALITY','POSTED_BY'])['SQUARE_FT'].transform('mean')[:29451]\ntest['LOCALITY_PB_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['LOCALITY','POSTED_BY'])['SQUARE_FT'].transform('mean')[29451:]","execution_count":53,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['LOCALITY_BHKNO_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['LOCALITY','BHK_NO.'])['SQUARE_FT'].transform('mean')[:29451]\ntest['LOCALITY_BHKNO_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['LOCALITY','BHK_NO.'])['SQUARE_FT'].transform('mean')[29451:]","execution_count":54,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import KFold\nfold = KFold(n_splits=4, shuffle=True, random_state=2020)\nfor train_index, test_index in fold.split(train, train['TARGET(PRICE_IN_LACS)']):\n    encoding=train.iloc[train_index].groupby('LOCALITY')['TARGET(PRICE_IN_LACS)'].mean()\n    train.loc[test_index,'NLOCALITY']=train.loc[test_index,'LOCALITY'].map(encoding)\nencoding=train.groupby('LOCALITY')['TARGET(PRICE_IN_LACS)'].mean()\ntest['NLOCALITY']=test['LOCALITY'].map(encoding)","execution_count":55,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.info()\ntest.info()","execution_count":56,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 29451 entries, 0 to 29450\nData columns (total 24 columns):\n #   Column                  Non-Null Count  Dtype  \n---  ------                  --------------  -----  \n 0   POSTED_BY               29451 non-null  int64  \n 1   UNDER_CONSTRUCTION      29451 non-null  int64  \n 2   RERA                    29451 non-null  int64  \n 3   BHK_NO.                 29451 non-null  int64  \n 4   BHK_OR_RK               29451 non-null  int64  \n 5   SQUARE_FT               29451 non-null  float64\n 6   READY_TO_MOVE           29451 non-null  int64  \n 7   RESALE                  29451 non-null  int64  \n 8   ADDRESS                 29451 non-null  object \n 9   LONGITUDE               29451 non-null  float64\n 10  LATITUDE                29451 non-null  float64\n 11  TARGET(PRICE_IN_LACS)   29451 non-null  float64\n 12  SQUARE_FT_PER_ROOM      29451 non-null  float64\n 13  SQUARE_FT_ROOM          29451 non-null  float64\n 14  CITY                    29451 non-null  object \n 15  LOCALITY                29451 non-null  object \n 16  LOCALITY_SF_MEAN        29451 non-null  float64\n 17  LOCALITY_SF_MIN         29451 non-null  float64\n 18  LOCALITY_SF_MAX         29451 non-null  float64\n 19  LOCALITY_SF_STD         27776 non-null  float64\n 20  LOCALITY_FT_SF_MEAN     29451 non-null  float64\n 21  LOCALITY_PB_SF_MEAN     29451 non-null  float64\n 22  LOCALITY_BHKNO_SF_MEAN  29451 non-null  float64\n 23  NLOCALITY               25776 non-null  float64\ndtypes: float64(14), int64(7), object(3)\nmemory usage: 5.4+ MB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 68720 entries, 0 to 68719\nData columns (total 23 columns):\n #   Column                  Non-Null Count  Dtype  \n---  ------                  --------------  -----  \n 0   POSTED_BY               68720 non-null  int64  \n 1   UNDER_CONSTRUCTION      68720 non-null  int64  \n 2   RERA                    68720 non-null  int64  \n 3   BHK_NO.                 68720 non-null  int64  \n 4   BHK_OR_RK               68720 non-null  int64  \n 5   SQUARE_FT               68720 non-null  float64\n 6   READY_TO_MOVE           68720 non-null  int64  \n 7   RESALE                  68720 non-null  int64  \n 8   ADDRESS                 68720 non-null  object \n 9   LONGITUDE               68720 non-null  float64\n 10  LATITUDE                68720 non-null  float64\n 11  SQUARE_FT_PER_ROOM      68720 non-null  float64\n 12  SQUARE_FT_ROOM          68720 non-null  float64\n 13  CITY                    68720 non-null  object \n 14  LOCALITY                68720 non-null  object \n 15  LOCALITY_SF_MEAN        68720 non-null  float64\n 16  LOCALITY_SF_MIN         68720 non-null  float64\n 17  LOCALITY_SF_MAX         68720 non-null  float64\n 18  LOCALITY_SF_STD         64720 non-null  float64\n 19  LOCALITY_FT_SF_MEAN     68720 non-null  float64\n 20  LOCALITY_PB_SF_MEAN     68720 non-null  float64\n 21  LOCALITY_BHKNO_SF_MEAN  68720 non-null  float64\n 22  NLOCALITY               61156 non-null  float64\ndtypes: float64(13), int64(7), object(3)\nmemory usage: 12.1+ MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=train.drop('LOCALITY',axis=1)\ntest=test.drop('LOCALITY',axis=1)","execution_count":57,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_SF_MEAN']=train['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].mean())\ntest['CITY_SF_MEAN']=test['CITY'].map(pd.concat([train,test],axis=0)\n                                         .groupby('CITY')['SQUARE_FT'].mean())","execution_count":58,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_SF_MIN']=train['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].min())\ntest['CITY_SF_MIN']=test['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].min())","execution_count":59,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_SF_MAX']=train['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].max())\ntest['CITY_SF_MAX']=test['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].max())","execution_count":60,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_SF_STD']=train['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].std())\ntest['CITY_SF_STD']=test['CITY'].map(pd.concat([train,test],axis=0)\n                                        .groupby('CITY')['SQUARE_FT'].std())","execution_count":61,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_PB_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['CITY','POSTED_BY'])['SQUARE_FT'].transform('mean')[:29451]\ntest['CITY_PB_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['CITY','POSTED_BY'])['SQUARE_FT'].transform('mean')[29451:]","execution_count":62,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_FT_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['CITY','BHK_OR_RK'])['SQUARE_FT'].transform('mean')[:29451]\ntest['CITY_FT_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['CITY','BHK_OR_RK'])['SQUARE_FT'].transform('mean')[29451:]","execution_count":63,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['CITY_BHKNO_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['CITY','BHK_NO.'])['SQUARE_FT'].transform('mean')[:29451]\ntest['CITY_BHKNO_SF_MEAN']=pd.concat([train,test],axis=0).groupby(['CITY','BHK_NO.'])['SQUARE_FT'].transform('mean')[29451:]","execution_count":64,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"fold = KFold(n_splits=5, shuffle=True, random_state=2020)\nfor train_index, test_index in fold.split(train, train['TARGET(PRICE_IN_LACS)']):\n    encoding=train.iloc[train_index].groupby('CITY')['TARGET(PRICE_IN_LACS)'].mean()\n    train.loc[test_index,'NCITY']=train.loc[test_index,'CITY'].map(encoding)\nencoding=train.groupby('CITY')['TARGET(PRICE_IN_LACS)'].mean()\ntest['NCITY']=test['CITY'].map(encoding)","execution_count":65,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=train.drop('CITY',axis=1)\ntest=test.drop('CITY',axis=1)","execution_count":66,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train['X']=np.cos(train['LATITUDE'])*np.cos(train['LONGITUDE'])\ntrain['Y']=np.cos(train['LATITUDE'])*np.sin(train['LONGITUDE'])\ntrain['Z']=np.sin(train['LATITUDE'])\ntest['X']=np.cos(test['LATITUDE'])*np.cos(test['LONGITUDE'])\ntest['Y']=np.cos(test['LATITUDE'])*np.sin(test['LONGITUDE'])\ntest['Z']=np.sin(test['LATITUDE'])","execution_count":67,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"drop_cols=['POSTED_BY', 'UNDER_CONSTRUCTION', 'RERA', 'BHK_NO.', 'BHK_OR_RK', 'SQUARE_FT', 'READY_TO_MOVE', 'RESALE', 'LONGITUDE', 'LATITUDE','TARGET(PRICE_IN_LACS)', 'SQUARE_FT_PER_ROOM','SQUARE_FT_ROOM','CITY','X','Y','Z']","execution_count":68,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=train.drop(['ADDRESS'],axis=1)\ntest=test.drop(['ADDRESS'],axis=1)","execution_count":69,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=train.drop_duplicates()\ntrain.shape","execution_count":70,"outputs":[{"output_type":"execute_result","execution_count":70,"data":{"text/plain":"(29384, 32)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.info()\ntest.info()","execution_count":71,"outputs":[{"output_type":"stream","text":"<class 'pandas.core.frame.DataFrame'>\nInt64Index: 29384 entries, 0 to 29450\nData columns (total 32 columns):\n #   Column                  Non-Null Count  Dtype  \n---  ------                  --------------  -----  \n 0   POSTED_BY               29384 non-null  int64  \n 1   UNDER_CONSTRUCTION      29384 non-null  int64  \n 2   RERA                    29384 non-null  int64  \n 3   BHK_NO.                 29384 non-null  int64  \n 4   BHK_OR_RK               29384 non-null  int64  \n 5   SQUARE_FT               29384 non-null  float64\n 6   READY_TO_MOVE           29384 non-null  int64  \n 7   RESALE                  29384 non-null  int64  \n 8   LONGITUDE               29384 non-null  float64\n 9   LATITUDE                29384 non-null  float64\n 10  TARGET(PRICE_IN_LACS)   29384 non-null  float64\n 11  SQUARE_FT_PER_ROOM      29384 non-null  float64\n 12  SQUARE_FT_ROOM          29384 non-null  float64\n 13  LOCALITY_SF_MEAN        29384 non-null  float64\n 14  LOCALITY_SF_MIN         29384 non-null  float64\n 15  LOCALITY_SF_MAX         29384 non-null  float64\n 16  LOCALITY_SF_STD         27709 non-null  float64\n 17  LOCALITY_FT_SF_MEAN     29384 non-null  float64\n 18  LOCALITY_PB_SF_MEAN     29384 non-null  float64\n 19  LOCALITY_BHKNO_SF_MEAN  29384 non-null  float64\n 20  NLOCALITY               25711 non-null  float64\n 21  CITY_SF_MEAN            29384 non-null  float64\n 22  CITY_SF_MIN             29384 non-null  float64\n 23  CITY_SF_MAX             29384 non-null  float64\n 24  CITY_SF_STD             29364 non-null  float64\n 25  CITY_PB_SF_MEAN         29384 non-null  float64\n 26  CITY_FT_SF_MEAN         29384 non-null  float64\n 27  CITY_BHKNO_SF_MEAN      29384 non-null  float64\n 28  NCITY                   29306 non-null  float64\n 29  X                       29384 non-null  float64\n 30  Y                       29384 non-null  float64\n 31  Z                       29384 non-null  float64\ndtypes: float64(25), int64(7)\nmemory usage: 7.4 MB\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 68720 entries, 0 to 68719\nData columns (total 31 columns):\n #   Column                  Non-Null Count  Dtype  \n---  ------                  --------------  -----  \n 0   POSTED_BY               68720 non-null  int64  \n 1   UNDER_CONSTRUCTION      68720 non-null  int64  \n 2   RERA                    68720 non-null  int64  \n 3   BHK_NO.                 68720 non-null  int64  \n 4   BHK_OR_RK               68720 non-null  int64  \n 5   SQUARE_FT               68720 non-null  float64\n 6   READY_TO_MOVE           68720 non-null  int64  \n 7   RESALE                  68720 non-null  int64  \n 8   LONGITUDE               68720 non-null  float64\n 9   LATITUDE                68720 non-null  float64\n 10  SQUARE_FT_PER_ROOM      68720 non-null  float64\n 11  SQUARE_FT_ROOM          68720 non-null  float64\n 12  LOCALITY_SF_MEAN        68720 non-null  float64\n 13  LOCALITY_SF_MIN         68720 non-null  float64\n 14  LOCALITY_SF_MAX         68720 non-null  float64\n 15  LOCALITY_SF_STD         64720 non-null  float64\n 16  LOCALITY_FT_SF_MEAN     68720 non-null  float64\n 17  LOCALITY_PB_SF_MEAN     68720 non-null  float64\n 18  LOCALITY_BHKNO_SF_MEAN  68720 non-null  float64\n 19  NLOCALITY               61156 non-null  float64\n 20  CITY_SF_MEAN            68720 non-null  float64\n 21  CITY_SF_MIN             68720 non-null  float64\n 22  CITY_SF_MAX             68720 non-null  float64\n 23  CITY_SF_STD             68693 non-null  float64\n 24  CITY_PB_SF_MEAN         68720 non-null  float64\n 25  CITY_FT_SF_MEAN         68720 non-null  float64\n 26  CITY_BHKNO_SF_MEAN      68720 non-null  float64\n 27  NCITY                   68595 non-null  float64\n 28  X                       68720 non-null  float64\n 29  Y                       68720 non-null  float64\n 30  Z                       68720 non-null  float64\ndtypes: float64(24), int64(7)\nmemory usage: 16.3 MB\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.corr()[abs(train.corr())>0.4]","execution_count":72,"outputs":[{"output_type":"execute_result","execution_count":72,"data":{"text/plain":"                        POSTED_BY  UNDER_CONSTRUCTION  RERA   BHK_NO.  BHK_OR_RK  SQUARE_FT  READY_TO_MOVE  RESALE  LONGITUDE  LATITUDE  TARGET(PRICE_IN_LACS)  SQUARE_FT_PER_ROOM  SQUARE_FT_ROOM  LOCALITY_SF_MEAN  LOCALITY_SF_MIN  LOCALITY_SF_MAX  LOCALITY_SF_STD  LOCALITY_FT_SF_MEAN  LOCALITY_PB_SF_MEAN  LOCALITY_BHKNO_SF_MEAN  NLOCALITY  CITY_SF_MEAN  CITY_SF_MIN  CITY_SF_MAX  CITY_SF_STD  CITY_PB_SF_MEAN  CITY_FT_SF_MEAN  CITY_BHKNO_SF_MEAN     NCITY         X         Y    Z\nPOSTED_BY                1.000000                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN  0.401657       NaN       NaN  NaN\nUNDER_CONSTRUCTION            NaN                 1.0   NaN       NaN        NaN        NaN           -1.0     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nRERA                          NaN                 NaN   1.0       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nBHK_NO.                       NaN                 NaN   NaN  1.000000        NaN   0.668176            NaN     NaN        NaN       NaN               0.502076           -0.875626        0.983932          0.477522              NaN              NaN              NaN             0.478134             0.484628                0.833066        NaN           NaN          NaN          NaN          NaN              NaN              NaN            0.958313       NaN       NaN       NaN  NaN\nBHK_OR_RK                     NaN                 NaN   NaN       NaN        1.0        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nSQUARE_FT                     NaN                 NaN   NaN  0.668176        NaN   1.000000            NaN     NaN        NaN       NaN               0.638905           -0.491322        0.772245          0.592725              NaN              NaN              NaN             0.594303             0.632369                0.802524        NaN           NaN          NaN          NaN          NaN              NaN              NaN            0.696048       NaN       NaN       NaN  NaN\nREADY_TO_MOVE                 NaN                -1.0   NaN       NaN        NaN        NaN            1.0     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nRESALE                        NaN                 NaN   NaN       NaN        NaN        NaN            NaN     1.0        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nLONGITUDE                     NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        1.0       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nLATITUDE                      NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN  1.000000                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN    -0.523943          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nTARGET(PRICE_IN_LACS)         NaN                 NaN   NaN  0.502076        NaN   0.638905            NaN     NaN        NaN       NaN               1.000000                 NaN        0.563774          0.412235              NaN              NaN              NaN             0.412720             0.432324                0.551403   0.755887           NaN          NaN          NaN          NaN              NaN              NaN            0.503288  0.547456       NaN       NaN  NaN\nSQUARE_FT_PER_ROOM            NaN                 NaN   NaN -0.875626        NaN  -0.491322            NaN     NaN        NaN       NaN                    NaN            1.000000       -0.815193               NaN              NaN              NaN              NaN                  NaN                  NaN               -0.709167        NaN           NaN          NaN          NaN          NaN              NaN              NaN           -0.863051       NaN       NaN       NaN  NaN\nSQUARE_FT_ROOM                NaN                 NaN   NaN  0.983932        NaN   0.772245            NaN     NaN        NaN       NaN               0.563774           -0.815193        1.000000          0.525144              NaN              NaN              NaN             0.525575             0.540386                0.870072        NaN           NaN          NaN          NaN          NaN              NaN              NaN            0.951776       NaN       NaN       NaN  NaN\nLOCALITY_SF_MEAN              NaN                 NaN   NaN  0.477522        NaN   0.592725            NaN     NaN        NaN       NaN               0.412235                 NaN        0.525144          1.000000         0.494709         0.434807              NaN             0.997359             0.932485                0.737731   0.480956      0.508732          NaN          NaN          NaN         0.529238         0.506580            0.547096       NaN       NaN       NaN  NaN\nLOCALITY_SF_MIN               NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN          0.494709         1.000000              NaN              NaN             0.493537             0.463014                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nLOCALITY_SF_MAX               NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN          0.434807              NaN         1.000000         0.712581             0.433840             0.405274                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nLOCALITY_SF_STD               NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN         0.712581         1.000000                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nLOCALITY_FT_SF_MEAN           NaN                 NaN   NaN  0.478134        NaN   0.594303            NaN     NaN        NaN       NaN               0.412720                 NaN        0.525575          0.997359         0.493537         0.433840              NaN             1.000000             0.930608                0.737993   0.478457      0.507284          NaN          NaN          NaN         0.528082         0.515396            0.547441       NaN       NaN       NaN  NaN\nLOCALITY_PB_SF_MEAN           NaN                 NaN   NaN  0.484628        NaN   0.632369            NaN     NaN        NaN       NaN               0.432324                 NaN        0.540386          0.932485         0.463014         0.405274              NaN             0.930608             1.000000                0.741497   0.435888      0.478344          NaN          NaN          NaN         0.545854         0.477367            0.549952       NaN       NaN       NaN  NaN\nLOCALITY_BHKNO_SF_MEAN        NaN                 NaN   NaN  0.833066        NaN   0.802524            NaN     NaN        NaN       NaN               0.551403           -0.709167        0.870072          0.737731              NaN              NaN              NaN             0.737993             0.741497                1.000000        NaN           NaN          NaN          NaN          NaN         0.426004              NaN            0.867586       NaN       NaN       NaN  NaN\nNLOCALITY                     NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN               0.755887                 NaN             NaN          0.480956              NaN              NaN              NaN             0.478457             0.435888                     NaN   1.000000           NaN          NaN          NaN     0.449432              NaN              NaN                 NaN  0.633628       NaN       NaN  NaN\nCITY_SF_MEAN                  NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN          0.508732              NaN              NaN              NaN             0.507284             0.478344                     NaN        NaN      1.000000          NaN          NaN          NaN         0.868847         0.987185            0.451499       NaN       NaN       NaN  NaN\nCITY_SF_MIN                   NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          1.0          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  NaN\nCITY_SF_MAX                   NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN -0.523943                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN     1.000000          NaN              NaN              NaN                 NaN       NaN       NaN  0.452412  NaN\nCITY_SF_STD                   NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN   0.449432           NaN          NaN          NaN     1.000000              NaN              NaN                 NaN  0.653532       NaN       NaN  NaN\nCITY_PB_SF_MEAN               NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN          0.529238              NaN              NaN              NaN             0.528082             0.545854                0.426004        NaN      0.868847          NaN          NaN          NaN         1.000000         0.859006            0.467057       NaN       NaN       NaN  NaN\nCITY_FT_SF_MEAN               NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN          0.506580              NaN              NaN              NaN             0.515396             0.477367                     NaN        NaN      0.987185          NaN          NaN          NaN         0.859006         1.000000            0.451716       NaN       NaN       NaN  NaN\nCITY_BHKNO_SF_MEAN            NaN                 NaN   NaN  0.958313        NaN   0.696048            NaN     NaN        NaN       NaN               0.503288           -0.863051        0.951776          0.547096              NaN              NaN              NaN             0.547441             0.549952                0.867586        NaN      0.451499          NaN          NaN          NaN         0.467057         0.451716            1.000000       NaN       NaN       NaN  NaN\nNCITY                    0.401657                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN               0.547456                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN   0.633628           NaN          NaN          NaN     0.653532              NaN              NaN                 NaN  1.000000 -0.483153       NaN  NaN\nX                             NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN -0.483153  1.000000       NaN  NaN\nY                             NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN     0.452412          NaN              NaN              NaN                 NaN       NaN       NaN  1.000000  NaN\nZ                             NaN                 NaN   NaN       NaN        NaN        NaN            NaN     NaN        NaN       NaN                    NaN                 NaN             NaN               NaN              NaN              NaN              NaN                  NaN                  NaN                     NaN        NaN           NaN          NaN          NaN          NaN              NaN              NaN                 NaN       NaN       NaN       NaN  1.0","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>POSTED_BY</th>\n      <th>UNDER_CONSTRUCTION</th>\n      <th>RERA</th>\n      <th>BHK_NO.</th>\n      <th>BHK_OR_RK</th>\n      <th>SQUARE_FT</th>\n      <th>READY_TO_MOVE</th>\n      <th>RESALE</th>\n      <th>LONGITUDE</th>\n      <th>LATITUDE</th>\n      <th>TARGET(PRICE_IN_LACS)</th>\n      <th>SQUARE_FT_PER_ROOM</th>\n      <th>SQUARE_FT_ROOM</th>\n      <th>LOCALITY_SF_MEAN</th>\n      <th>LOCALITY_SF_MIN</th>\n      <th>LOCALITY_SF_MAX</th>\n      <th>LOCALITY_SF_STD</th>\n      <th>LOCALITY_FT_SF_MEAN</th>\n      <th>LOCALITY_PB_SF_MEAN</th>\n      <th>LOCALITY_BHKNO_SF_MEAN</th>\n      <th>NLOCALITY</th>\n      <th>CITY_SF_MEAN</th>\n      <th>CITY_SF_MIN</th>\n      <th>CITY_SF_MAX</th>\n      <th>CITY_SF_STD</th>\n      <th>CITY_PB_SF_MEAN</th>\n      <th>CITY_FT_SF_MEAN</th>\n      <th>CITY_BHKNO_SF_MEAN</th>\n      <th>NCITY</th>\n      <th>X</th>\n      <th>Y</th>\n      <th>Z</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>POSTED_BY</th>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.401657</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>UNDER_CONSTRUCTION</th>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>RERA</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>BHK_NO.</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.668176</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.502076</td>\n      <td>-0.875626</td>\n      <td>0.983932</td>\n      <td>0.477522</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.478134</td>\n      <td>0.484628</td>\n      <td>0.833066</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.958313</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>BHK_OR_RK</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>SQUARE_FT</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.668176</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.638905</td>\n      <td>-0.491322</td>\n      <td>0.772245</td>\n      <td>0.592725</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.594303</td>\n      <td>0.632369</td>\n      <td>0.802524</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.696048</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>READY_TO_MOVE</th>\n      <td>NaN</td>\n      <td>-1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>RESALE</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LONGITUDE</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LATITUDE</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.523943</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>TARGET(PRICE_IN_LACS)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.502076</td>\n      <td>NaN</td>\n      <td>0.638905</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.563774</td>\n      <td>0.412235</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.412720</td>\n      <td>0.432324</td>\n      <td>0.551403</td>\n      <td>0.755887</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.503288</td>\n      <td>0.547456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>SQUARE_FT_PER_ROOM</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.875626</td>\n      <td>NaN</td>\n      <td>-0.491322</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>-0.815193</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.709167</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.863051</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>SQUARE_FT_ROOM</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.983932</td>\n      <td>NaN</td>\n      <td>0.772245</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.563774</td>\n      <td>-0.815193</td>\n      <td>1.000000</td>\n      <td>0.525144</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.525575</td>\n      <td>0.540386</td>\n      <td>0.870072</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.951776</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.477522</td>\n      <td>NaN</td>\n      <td>0.592725</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.412235</td>\n      <td>NaN</td>\n      <td>0.525144</td>\n      <td>1.000000</td>\n      <td>0.494709</td>\n      <td>0.434807</td>\n      <td>NaN</td>\n      <td>0.997359</td>\n      <td>0.932485</td>\n      <td>0.737731</td>\n      <td>0.480956</td>\n      <td>0.508732</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.529238</td>\n      <td>0.506580</td>\n      <td>0.547096</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_SF_MIN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.494709</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.493537</td>\n      <td>0.463014</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_SF_MAX</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.434807</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.712581</td>\n      <td>0.433840</td>\n      <td>0.405274</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_SF_STD</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.712581</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_FT_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.478134</td>\n      <td>NaN</td>\n      <td>0.594303</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.412720</td>\n      <td>NaN</td>\n      <td>0.525575</td>\n      <td>0.997359</td>\n      <td>0.493537</td>\n      <td>0.433840</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.930608</td>\n      <td>0.737993</td>\n      <td>0.478457</td>\n      <td>0.507284</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.528082</td>\n      <td>0.515396</td>\n      <td>0.547441</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_PB_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.484628</td>\n      <td>NaN</td>\n      <td>0.632369</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.432324</td>\n      <td>NaN</td>\n      <td>0.540386</td>\n      <td>0.932485</td>\n      <td>0.463014</td>\n      <td>0.405274</td>\n      <td>NaN</td>\n      <td>0.930608</td>\n      <td>1.000000</td>\n      <td>0.741497</td>\n      <td>0.435888</td>\n      <td>0.478344</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.545854</td>\n      <td>0.477367</td>\n      <td>0.549952</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>LOCALITY_BHKNO_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.833066</td>\n      <td>NaN</td>\n      <td>0.802524</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.551403</td>\n      <td>-0.709167</td>\n      <td>0.870072</td>\n      <td>0.737731</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.737993</td>\n      <td>0.741497</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.426004</td>\n      <td>NaN</td>\n      <td>0.867586</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>NLOCALITY</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.755887</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.480956</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.478457</td>\n      <td>0.435888</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.449432</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.633628</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.508732</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.507284</td>\n      <td>0.478344</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.868847</td>\n      <td>0.987185</td>\n      <td>0.451499</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_SF_MIN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_SF_MAX</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.523943</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.452412</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_SF_STD</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.449432</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.653532</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_PB_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.529238</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.528082</td>\n      <td>0.545854</td>\n      <td>0.426004</td>\n      <td>NaN</td>\n      <td>0.868847</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.859006</td>\n      <td>0.467057</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_FT_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.506580</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.515396</td>\n      <td>0.477367</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.987185</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.859006</td>\n      <td>1.000000</td>\n      <td>0.451716</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>CITY_BHKNO_SF_MEAN</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.958313</td>\n      <td>NaN</td>\n      <td>0.696048</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.503288</td>\n      <td>-0.863051</td>\n      <td>0.951776</td>\n      <td>0.547096</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.547441</td>\n      <td>0.549952</td>\n      <td>0.867586</td>\n      <td>NaN</td>\n      <td>0.451499</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.467057</td>\n      <td>0.451716</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>NCITY</th>\n      <td>0.401657</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.547456</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.633628</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.653532</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>-0.483153</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>X</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.483153</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Y</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.452412</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Z</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.figure(figsize=(15,8))\nsns.heatmap(train.corr(),cmap=sns.diverging_palette(20, 220, n=200));","execution_count":73,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1080x576 with 2 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\n"},"metadata":{"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"cat_cols=['UNDER_CONSTRUCTION','POSTED_BY','RERA','BHK_NO.','BHK_OR_RK','READY_TO_MOVE','RESALE']","execution_count":74,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y=train['TARGET(PRICE_IN_LACS)']\nX=train.drop(['TARGET(PRICE_IN_LACS)'],axis=1)","execution_count":75,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Kfold LGBM"},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time \nfrom sklearn.model_selection import KFold\nerr = [] \ny_pred_tot_lgbm = np.zeros((len(test), ))\n\n\nfold = KFold(n_splits=10, shuffle=True, random_state=2020)\ni = 1\n\nfor train_index, test_index in fold.split(X, y):\n    x_train, x_val = X.iloc[train_index], X.iloc[test_index]\n    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n    m = LGBMRegressor(n_estimators=5000,importance_type='gain')\n    m.fit(x_train, y_train,eval_set=[(x_val, y_val)],early_stopping_rounds=50, categorical_feature=cat_cols,verbose=0)\n    pred_val = m.predict(x_val)\n    print(i, \" err_lgm: \", np.sqrt(mean_squared_log_error(np.exp(y_val)-1,np.exp(pred_val)-1)))\n    err.append(np.sqrt(mean_squared_log_error(np.exp(y_val)-1,np.exp(pred_val)-1)))\n    y_pred_tot_lgbm+= m.predict(test)\n    i = i + 1\ny_pred_tot_lgbm=y_pred_tot_lgbm/10\nsum(err)/10\n","execution_count":77,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"1  err_lgm:  0.2700085867945723\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"2  err_lgm:  0.29499302782312364\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"3  err_lgm:  0.2748198662360566\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"4  err_lgm:  0.286286485689435\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"5  err_lgm:  0.2674966857761276\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"6  err_lgm:  0.2996768019915952\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"7  err_lgm:  0.27907175357869207\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"8  err_lgm:  0.25030142962007706\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"9  err_lgm:  0.2699603322870716\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/lightgbm/basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['BHK_NO.', 'BHK_OR_RK', 'POSTED_BY', 'READY_TO_MOVE', 'RERA', 'RESALE', 'UNDER_CONSTRUCTION']\n  'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n","name":"stderr"},{"output_type":"stream","text":"10  err_lgm:  0.2714572438733645\nCPU times: user 2min 30s, sys: 1.05 s, total: 2min 31s\nWall time: 39.4 s\n","name":"stdout"},{"output_type":"execute_result","execution_count":77,"data":{"text/plain":"0.27640722136701157"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# writing prediction of lgbm to a csv file\n# sub['TARGET(PRICE_IN_LACS)']=np.exp(y_pred_tot_lgbm)-1\n# sub.to_csv('lgbm_kfold.csv',index=False)","execution_count":78,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Kfold XgBoost"},{"metadata":{"trusted":true},"cell_type":"code","source":"err = [] \ny_pred_tot_xgb = np.zeros((len(test), ))\n\n\nfold = KFold(n_splits=10, shuffle=True, random_state=2020)\ni = 1\n\nfor train_index, test_index in fold.split(X, y):\n    x_train, x_val = X.iloc[train_index], X.iloc[test_index]\n    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n    m = XGBRegressor(n_estimators=1001)\n    m.fit(x_train, y_train,eval_set=[(x_val, y_val)],early_stopping_rounds=50,verbose=0)\n    pred_val = m.predict(x_val)\n    print(i, \" err_xgb: \", np.sqrt(mean_squared_log_error(np.exp(y_val)-1,np.exp(pred_val)-1)))\n    err.append(np.sqrt(mean_squared_log_error(np.exp(y_val)-1,np.exp(pred_val)-1)))\n    y_pred_tot_xgb+= m.predict(test)\n    i = i + 1\ny_pred_tot_xgb=y_pred_tot_xgb/10\nsum(err)/10","execution_count":79,"outputs":[{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"1  err_xgb:  0.2652555963380819\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"2  err_xgb:  0.2957315211729568\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"3  err_xgb:  0.2694949105820445\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"4  err_xgb:  0.2912778694759418\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"5  err_xgb:  0.274663725575146\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"6  err_xgb:  0.3072236245374282\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"7  err_xgb:  0.29145716034095387\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"8  err_xgb:  0.2569950050799101\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"9  err_xgb:  0.276474953936679\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n             importance_type='gain', interaction_constraints='',\n             learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n             min_child_weight=1, missing=nan, monotone_constraints='()',\n             n_estimators=1001, n_jobs=0, num_parallel_tree=1, random_state=0,\n             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n             tree_method='exact', validate_parameters=1, verbosity=None)"},"metadata":{}},{"output_type":"stream","text":"10  err_xgb:  0.272172898809599\n","name":"stdout"},{"output_type":"execute_result","execution_count":79,"data":{"text/plain":"0.2800747265848741"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# writing prediction of xgboost to a csv file\n# sub['TARGET(PRICE_IN_LACS)']=np.exp(y_pred_tot_xgb)-1\n# sub.to_csv('xgb_kfold.csv',index=False)","execution_count":80,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"kfold catboost"},{"metadata":{"trusted":true},"cell_type":"code","source":"err = [] \ny_pred_tot_catb = np.zeros((len(test), ))\n\n\nfold = KFold(n_splits=10, shuffle=True, random_state=2020)\ni = 1\n\nfor train_index, test_index in fold.split(X, y):\n    x_train, x_val = X.iloc[train_index], X.iloc[test_index]\n    y_train, y_val = y.iloc[train_index], y.iloc[test_index]\n    m = CatBoostRegressor(n_estimators=5000)\n    m.fit(x_train, y_train,eval_set=[(x_val, y_val)],cat_features=cat_cols,early_stopping_rounds=50,verbose=0)\n    pred_val = m.predict(x_val)\n    print(i, \" err_catb: \", np.sqrt(mean_squared_log_error(np.exp(y_val)-1,np.exp(pred_val)-1)))\n    err.append(np.sqrt(mean_squared_log_error(np.exp(y_val)-1,np.exp(pred_val)-1)))\n    y_pred_tot_catb+= m.predict(test)\n    i = i + 1\ny_pred_tot_catb=y_pred_tot_catb/10\nsum(err)/10","execution_count":81,"outputs":[{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f014920de10>"},"metadata":{}},{"output_type":"stream","text":"1  err_catb:  0.26110565652048007\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f0148987c90>"},"metadata":{}},{"output_type":"stream","text":"2  err_catb:  0.29477836665769525\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f014920dcd0>"},"metadata":{}},{"output_type":"stream","text":"3  err_catb:  0.2740556567109096\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f0149234a90>"},"metadata":{}},{"output_type":"stream","text":"4  err_catb:  0.2861999314849433\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f0149234a50>"},"metadata":{}},{"output_type":"stream","text":"5  err_catb:  0.26677331562209683\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f014898f750>"},"metadata":{}},{"output_type":"stream","text":"6  err_catb:  0.2859328419069842\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f01492347d0>"},"metadata":{}},{"output_type":"stream","text":"7  err_catb:  0.28143982458641165\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f0149234a10>"},"metadata":{}},{"output_type":"stream","text":"8  err_catb:  0.24977836602593637\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f014a1ad9d0>"},"metadata":{}},{"output_type":"stream","text":"9  err_catb:  0.27165680112243323\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"<catboost.core.CatBoostRegressor at 0x7f014898f090>"},"metadata":{}},{"output_type":"stream","text":"10  err_catb:  0.2692244392115528\n","name":"stdout"},{"output_type":"execute_result","execution_count":81,"data":{"text/plain":"0.2740945199849444"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"# writing prediction of catboost to a csv file\n# sub['TARGET(PRICE_IN_LACS)']=np.exp(y_pred_tot_catb)-1\n# sub.to_csv('catb_kfold.csv',index=False)","execution_count":82,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# writing ensemble of above three predictions to a csv file\nsub['TARGET(PRICE_IN_LACS)']=np.exp((y_pred_tot_xgb*0.62+y_pred_tot_lgbm*0.19+y_pred_tot_catb*0.19))-1\nsub['TARGET(PRICE_IN_LACS)']=y_from_train.fillna(sub['TARGET(PRICE_IN_LACS)'])\nsub.to_csv('avg3.csv',index=False)","execution_count":83,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}